Therefore, the present paper performs an ablation study of post-exploration in both tabular and deep RL settings on both discrete and continuous tasks. Experiments in a range of MiniGrid and Mujoco tasks show that post-exploration provides a strong isolated benefit over standard IMGEP algorithms. As a smaller contribution, we also cast Go-Explore into the IMGEP framework, and show how it can be combined with hindsight experience replay (HER)  [1]} to make more efficient use of the observed data. In short, our work presents a systematic study of post-exploration, and shows its effectiveness on both discrete navigation tasks and continuous control tasks.
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